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A long term calibration and verification of a submerged aquatic vegetation model for Lake Okeechobee

机译:奥基乔比湖的水下水生植被模型的长期校准和验证

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Introduction Submerged aquatic vegetation (SAV) has multiple functions in Lake Okeechobee. It provides critical habitat for fish and wildlife, stabilizes sediments, reduces phosphorus (P) concentration in the water column by preventing re-suspension of P-rich sediments, and provides a substrate for attached algae, which also helps to remove P from the water column. Ten year water quality and SAV growth simulations are presented and compared with observed SAV and water quality data collected in the nearshore zone in Lake Okeechobee. Methods The SAV theory and approach used in the LOEM are modified from the Chesapeake Bay model and incorporate three state variables: shoots (above the bed sediment), roots (in the bed sediment), and epiphytes (attached to the shoots). The SAV model has direct linkages with the water quality model, including (1) a link between the growth and decay of SAV and the nutrient pool of the water quality model; (2) a link between the photosynthesis and respiration of SAV and dissolved oxygen dynamics, and (3) the ways in which settling of particulate organic matter and nutrient uptake affect nutrient levels in the water column and in the sediment bed. Results Total suspended solids affect light attenuation and are another major driving factor for SAV growth in the nearshore and littoral zone area. The model performs reasonably well in reproducing the spatial distribution of SAV. Conclusions The theoretical analysis and model sensitivity tests indicate that SAV growth is primarily controlled by light and nutrients. The light available for SAV growth depends on the water depth and the turbidity. In this full scale simulation, the water depth comes from the LOEM hydrodynamic model, and the turbidity depends on the suspended sediment concentration and algal concentration.
机译:简介在奥基乔比湖中,水下水生植物(SAV)具有多种功能。它为鱼类和野生生物提供了重要的栖息地,稳定了沉积物,通过防止富含磷的沉积物重新悬浮而降低了水柱中的磷浓度,并为附着的藻类提供了底物,这也有助于从水中去除磷柱。介绍了十年水质和SAV增长模拟,并将其与在奥基乔比湖近岸地区收集到的SAV和水质数据进行了比较。方法LOEM中使用的SAV理论和方法是根据切萨皮克湾模型进行修改的,并结合了三个状态变量:枝条(位于床层沉积物上方),根部(位于床层沉积物中)和附生植物(附于枝条上)。 SAV模型与水质模型有直接联系,包括(1)SAV的增长和衰减与水质模型的养分库之间的联系; (2)SAV的光合作用和呼吸作用与溶解氧动力学之间的联系,以及(3)颗粒有机物的沉降和养分吸收对水柱和沉积床中养分含量的影响方式。结果总悬浮固体影响光衰减,并且是近岸和沿海地区SAV增长的另一个主要驱动因素。该模型在再现SAV的空间分布方面表现合理。结论理论分析和模型敏感性测试表明SAV的生长主要受光和养分控制。 SAV生长所需的光取决于水深和浊度。在这种全面模拟中,水深来自LOEM流体动力学模型,浊度取决于悬浮的沉积物浓度和藻类浓度。

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